Tutorial Learning Objectives
In this tutorial you will learn how to:
- Learn to work with data within R studio
- Use simple commands in
R(e.g., subsetting, changing class, aggregating data) - Graph a simple bar chart
- Graph a time series
- Use simple commands in
- Observe trends in figures
Tree Swallow Nest Productivity
Before we jump into the weeds, let’s get to know our dataset!
The Tree Swallow (Tachycineta bicolor) is one of the most common birds in eastern North America that normally nests in tree cavities excavated by other species like woodpeckers, but also readily accepts human made nest boxes. Based on this quality and their abundance, Birds Canada has monitored nest boxes of tree swallows around the Long Point Biosphere Reserve, Ontario, from 1974 to 2014. Each year, May through June, volunteer research assistants check nest box contents daily, and band the adults and their young. Nest-box records are available from about 300 boxes from 3-4 sites during this period. Data collected includes nest box observations, clutch initiation dates, clutch size and egg weight, nest success, weather, insect abundance, and banding data. This data set includes all data entry related to eggs, nests, nestlings, nest check observations, and banding data from 1977 to 2014. More information on this dataset can be found here.
Additionally, in 2021, this dataset was quality checked and made open access by Jonathan Diamond through a Data Rescue internship with the Living Data Project, an initiative through the Canadian Institute of Ecology and Evolution that rescues legacy datasets.
Through Bird Studies Canada, Long Point Bird Observatory monitored three nest box “colonies” of Tree Swallows at Long Point, two on the “mainland” near Port Rowan (at the Port Rowan sewage lagoons and adjacent to agricultural land at mudd creek) and the third at the tip of the Point.
The Point
This colony was established in its present location in 1969. The nest boxes are located about 1 km west of the Tip of the point and are arranged 24.4m apart in a grid of numbered (north-south) rows and lettered (east-west) columns. Each box is designated by its position, so box 10G is in row 10 and column G. At the present time the rows in use are 1-19 and the letters are D-K, but many positions are unfilled and there are a total of 64 boxes.
The Sewage Lagoon
This colony was first established in 1977 and has since been expanded to a total of 77 boxes. The boxes are in two rows around the lagoon embankment, as well as across the street in a small cluster of 5 boxes.
Mud Creek
Established in 1987, the Mud Creek site is located 3.25 km north-northeast of Sewage Lagoon and contains 80 nest boxes. The habitat is an open, uncultivated field adjacent to a small woodlot.
Installing R
To navigate and complete the following tutorials you will be required to install R and we encourage you to install R Studio.
R is a freely available software and the most recent version of R can be downloaded from: https://cran.r-project.org. After you have installed R, we encourage you to download R Studio as it provides a more user friendly interface to interact with R. R Studio Desktop is freely available from https://rstudio.com/products/rstudio/download/.
The final piece of software that is required for completing the practicals is rmarkdown. Rmarkdown documents weave together narrative text and code to produce formatted, fully reproducible outputs. If you are unfamiliar with rmarkdown, a short tutorial is available from https://rmarkdown.rstudio.com/articles_intro.html.
Overview of R concepts
In this next section we are going to walk you through a few of the concepts you need to understand in order to work with data in R.
Importing data and packages
In order to work with certain set of data in R, you must first pull them into the program.
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# Before we can pull packages into R, you will first have to install them onto your computer. Run the following code to download the required packages (without the #s, you only need to install them once):
# install.packages("dplyr")
# install.packages("ggplot2")
# install.packages("tidyr")
# install.packages("lubridate")
# install.packages("reshape")
# install.packages("readr")
# Now we can start running those packages by calling on them using the following code:
library(dplyr)
library(ggplot2)
library(tidyr)
library(lubridate)
library(reshape)
library(readr)
# And lastly, we need to pull the actual datasets into R
banding <- read.csv("TRES/banding_final.csv") #this dataset set contains banding information
nest <- read.csv("TRES/nest_final.csv") #this dataset contains nest information
nestling <- read.csv("TRES/nestling_final.csv") #this dataset contains nestling informationData Exploration
Let’s do some data exploring! First, let’s see what is in the banding dataset. This will help give us a better idea of what we can look at.
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# look into the first few rows of the banding dataset. You can do this using the head() function
head(banding) #where head() is the function and banding is the dataset band_or_recapture band_number sex colour wing_chord wing_flat tail
1 R 1831.386 F BLU 116 119 58
2 R 1881.065 F BLU 115 119 54
3 R 2171.200 F BLU 112 114 55
4 R 1881.064 M BLU 123 124 60
5 B 2351.397 M BLU 118 119 58
6 R 1881.042 M BLU 123 125 56
p_9 weight date nest_box age_code year
1 89 22.0 2010-06-01 PT4G ASY 2010
2 90 19.6 2010-06-01 PT2F ASY 2010
3 88 21.5 2010-06-01 PT15G ASY 2010
4 96 21.1 2010-06-01 PT4G AHY 2010
5 92 22.1 2010-06-01 PT15G AHY 2010
6 97 20.5 2010-06-01 PT2F AHY 2010You can dig in deeper by using the summary() and str() functions
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# summarise the banding dataset
summary(banding) band_or_recapture band_number sex
Length:1602 Min. :1381 Length:1602
Class :character 1st Qu.:2312 Class :character
Mode :character Median :2351 Mode :character
Mean :2310
3rd Qu.:2402
Max. :5212
colour wing_chord wing_flat tail
Length:1602 Min. :100.0 Min. :102.0 Min. :44.00
Class :character 1st Qu.:113.0 1st Qu.:115.0 1st Qu.:53.00
Mode :character Median :116.0 Median :118.0 Median :55.00
Mean :115.7 Mean :117.9 Mean :54.98
3rd Qu.:118.0 3rd Qu.:121.0 3rd Qu.:57.00
Max. :129.0 Max. :130.0 Max. :69.00
p_9 weight date
Min. : 78.00 Min. :16.50 Length:1602
1st Qu.: 90.00 1st Qu.:19.80 Class :character
Median : 92.00 Median :20.50 Mode :character
Mean : 92.39 Mean :20.58
3rd Qu.: 95.00 3rd Qu.:21.40
Max. :109.00 Max. :25.90
nest_box age_code year
Length:1602 Length:1602 Min. :2010
Class :character Class :character 1st Qu.:2011
Mode :character Mode :character Median :2012
Mean :2012
3rd Qu.:2013
Max. :2014 Show code
# check the structure of the banding dataset
str(banding) #This is important, because the variables need to match what they are being used for (i.e., to calculate mean, the variable must be numeric, not a character type)'data.frame': 1602 obs. of 13 variables:
$ band_or_recapture: chr "R" "R" "R" "R" ...
$ band_number : num 1831 1881 2171 1881 2351 ...
$ sex : chr "F" "F" "F" "M" ...
$ colour : chr "BLU" "BLU" "BLU" "BLU" ...
$ wing_chord : num 116 115 112 123 118 123 110 111 112 117 ...
$ wing_flat : num 119 119 114 124 119 125 112 113 114 118 ...
$ tail : num 58 54 55 60 58 56 55 52 55 54 ...
$ p_9 : int 89 90 88 96 92 97 89 87 90 92 ...
$ weight : num 22 19.6 21.5 21.1 22.1 20.5 20 21.6 19.8 20.5 ...
$ date : chr "2010-06-01" "2010-06-01" "2010-06-01" "2010-06-01" ...
$ nest_box : chr "PT4G" "PT2F" "PT15G" "PT4G" ...
$ age_code : chr "ASY" "ASY" "ASY" "AHY" ...
$ year : int 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...Subsetting and conditional subsetting elements
The
[operator can be used to select multiple elements of an object: The[operator can be used to extract specific rows or columns from a data set where DATA[row, column]The
$operator can be used to extract elements by the element’s name
Let’s try pulling the first row from the banding dataset
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banding[c(1),] #notice how there is a comma after c(1)? This specifies we want to subset the first row! band_or_recapture band_number sex colour wing_chord wing_flat tail
1 R 1831.386 F BLU 116 119 58
p_9 weight date nest_box age_code year
1 89 22 2010-06-01 PT4G ASY 2010Let’s make a mini version of our banding dataset, and call it banding2, by subsetting rows 1 through 50 and columns 2, and 5 through 13
Appplying different functions to data
You can also run functions on different variables of your datasets which you can select by using $. You can wrap these in different functions to calculate various things. For example, let’s try calculating the mean weight from our banding2 dataset and assign it to a new variable called mean_weight.
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band_number wing_chord wing_flat tail p_9 weight date
1 1831.386 116 119 58 89 22.0 2010-06-01
2 1881.065 115 119 54 90 19.6 2010-06-01
3 2171.200 112 114 55 88 21.5 2010-06-01
4 1881.064 123 124 60 96 21.1 2010-06-01
5 2351.397 118 119 58 92 22.1 2010-06-01
6 1881.042 123 125 56 97 20.5 2010-06-01
nest_box age_code year mean_weight
1 PT4G ASY 2010 20.815
2 PT2F ASY 2010 20.815
3 PT15G ASY 2010 20.815
4 PT4G AHY 2010 20.815
5 PT15G AHY 2010 20.815
6 PT2F AHY 2010 20.815What if we wanted to calculate the mean weight of the Tree Swallows as recorded in the banding dataset based on their sex? We could do that by grouping how we calculate the mean by using the aggregate() function. The aggregate function can work to group data as follows:
aggregate(y ~ a + b + c + …, df, mean)
Where y is the variable you want to take the mean of, a, b, c… are variables that you are interested in grouping these means by, df is the dataframe that you are pulling these data from, and mean is instructing the command that the summary statistic you want to complete is the mean. Lets try it out!
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# If we wanted to look at the average weight of female and male birds in the banding dataset we would use aggregate() like this
banding3 <- aggregate(weight ~ sex, banding, mean) # banding3 is where these values will be stored
banding3 sex weight
1 F 20.41623
2 M 20.75646Try coding
# Try looking at the mean weight of Tree Swallows grouped by sex and year, call this new data frame 'banding4'Now that you have a good handle on basic subsetting, let’s dig a little deeper and use logical operators to further subset your data.
What if you want to focus in on looking at just one sex? How would you extract only data related to female birds from these data? One way to do this would be to use the subset() function and logical operators to separate out the data of interests from your data set.
- < (less than)
- <= (less than or equal to)
- > (greater than)
- >= (greater than or equal to)
- == (exactly equal to)
- != (not equal to)
- x | y (x OR y)
- x & y (x AND y)
It is important to note that certain logical operators only work on certain classes of data. For example, if we looked at sex (class of factor) we can’t subset values that are less than or equal to Female (this would make no sense since Female is not a number or integer!).
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band_or_recapture band_number sex colour wing_chord wing_flat tail
1 R 1831.386 F BLU 116 119 58
2 R 1881.065 F BLU 115 119 54
3 R 2171.200 F BLU 112 114 55
7 B 2351.397 F BLU 110 112 55
8 R 2321.083 F BLU 111 113 52
9 B 2351.397 F INT 112 114 55
p_9 weight date nest_box age_code year
1 89 22.0 2010-06-01 PT4G ASY 2010
2 90 19.6 2010-06-01 PT2F ASY 2010
3 88 21.5 2010-06-01 PT15G ASY 2010
7 89 20.0 2012-06-01 PT1J ASY 2010
8 87 21.6 2012-06-01 PT4J ASY 2010
9 90 19.8 2012-06-01 PT8K AHY 2010Remember, if you want to store this in a df to look at later I would have to assign it to a vector called “female_birds”
Notice how the vector female_birds changed from 853 observations to 495 observations?
Try coding
# Try to subset a dataframe called 'male_birds' that consists of male birds with the chord_length less than or equal to 150Question
How many male birds have a chord_length less than or equal to 150?
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# Answer = 749Try coding
# Try to subset a dataframe called 'male_birds' that consists of male birds with the chord_length less than or equal to 150Having fun yet? I know I am! Let’s look at the basics of a plot. This image was pulled from here.
Now that we are refreshed in the elements of a graph, let’s graph a relatively simple bar plot with our banding data frame.
Let’s look at the number of banded and recaptured birds there were each year. We will use ggplot to visualize the data.
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year
band_or_recapture 2010 2011 2012 2013 2014
B 62 125 118 68 101
R 179 264 292 139 254Show code
# Next, we can plot our table
ggplot(as.data.frame(tbl1), aes(x =factor(year), y = Freq, fill = band_or_recapture))+ #we've changed the format of our table to a dataframe so we can plot it.
geom_col(position = 'dodge') #geom_column is the type of graph, and position='dodge' allows us to visualize the barplots side by side.
Congrats! You’ve made your first graph. We can change elements of the graph by adding labels and titles, changing the theme and colours of our bars.
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# Add labels and change colours
ggplot(as.data.frame(tbl1), aes(x =factor(year), y = Freq, fill = band_or_recapture))+
geom_col(position = 'dodge') +
xlab('Year') +
ylab('Number of birds') +
scale_fill_manual(name= "Banded or Recaptured", values=c("B" = 'lightskyblue', "R" = 'plum3'))+
ggtitle("Birds banded or recaptured from 2010-2014") +
theme_classic() #gets rid of grey background
(If you’d like to learn more about ggplot, this tutorial is great!)
Question
What trends do you see?
Why do you think there are more recaptured birds compared to banded birds every year?
More data exploration
Next, let’s change gears and take a quick look at the egg nestling dataset.
# Lets get a sense of what columns are present in our dataset
head(nestling) nest_box band_number year clutch_number day_1_weight day_12_weight
1 PT7F 2351.39972 2014 NA <NA> <NA>
2 PT7F 2351.39973 2014 NA <NA> <NA>
3 PT7F 2351.39974 2014 NA <NA> <NA>
4 PT14G 2351.39975 2014 NA <NA> <NA>
5 PT14G 2351.39976 2014 NA <NA> <NA>
6 PT14G 2351.39977 2014 NA <NA> <NA>
site nest_code
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 <NA> <NA>
5 <NA> <NA>
6 <NA> <NA># Lets look at the structure of our new dataframe, nestling:
str(nestling)'data.frame': 32536 obs. of 8 variables:
$ nest_box : chr "PT7F" "PT7F" "PT7F" "PT14G" ...
$ band_number : chr "2351.39972" "2351.39973" "2351.39974" "2351.39975" ...
$ year : int 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
$ clutch_number: int NA NA NA NA NA NA NA NA NA NA ...
$ day_1_weight : chr NA NA NA NA ...
$ day_12_weight: chr NA NA NA NA ...
$ site : chr NA NA NA NA ...
$ nest_code : chr NA NA NA NA ...Show code
#We don't have entries for all rows of our dataframe. They will appear as **NA**s. We see the weight is a character vector. Let's change that to numeric using the as.numeric() function. Let's start #with the **day_1_weight** column:
nestling$day_1_weight <- as.numeric(nestling$day_1_weight)
# Now the **day_12_weight** column:
nestling$day_12_weight <- as.numeric(nestling$day_12_weight)
# Lets look at the structure of our new dataframe, nestling again:
str(nestling)'data.frame': 32536 obs. of 8 variables:
$ nest_box : chr "PT7F" "PT7F" "PT7F" "PT14G" ...
$ band_number : chr "2351.39972" "2351.39973" "2351.39974" "2351.39975" ...
$ year : int 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
$ clutch_number: int NA NA NA NA NA NA NA NA NA NA ...
$ day_1_weight : num NA NA NA NA NA NA NA NA NA NA ...
$ day_12_weight: num NA NA NA NA NA NA NA NA NA NA ...
$ site : chr NA NA NA NA ...
$ nest_code : chr NA NA NA NA ...We want to summarize our data so we can calculate the mean of each weight by year
nestling_weight <- nestling %>%
group_by(year) %>% #groups weights by year
filter(is.na(day_1_weight) == F, #gets rid of NAs
is.na(day_12_weight) == F) %>%
summarise(mean_day_1 = mean(day_1_weight), #calculates the mean of each year
mean_day_12 = mean(day_12_weight))
# We can convert the mean weights to long format, which gives us a weight column,
# with both weight variables, and a total column which contains the weights
nestling_weight2 <- gather(nestling_weight, weight, total, mean_day_1:mean_day_12)
#Look at the structure of our new dataframe, nestling_weight2
str(nestling_weight2)tibble [74 × 3] (S3: tbl_df/tbl/data.frame)
$ year : int [1:74] 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 ...
$ weight: chr [1:74] "mean_day_1" "mean_day_1" "mean_day_1" "mean_day_1" ...
$ total : num [1:74] 21.83 1.76 1.69 2.18 2.03 ...Ok, now we can plot it using ggplot.
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ggplot(data = nestling_weight2,
aes(x = year, y = total, group = weight)) + #Year goes on the x axis, weight(totals) on the y axis, and we group by each the means of each day
geom_line(aes(linetype = weight, color = weight)) + #aes changes the aesthetics of the lines so that linetype and colors are different from each other
theme_classic()
Hmm, looks like something is not quite right in our plot. There seems to be an outlier within the data. If we assume this is a data entry error, we can get rid of it. Since it looks like an earlier date, let’s just look at the first few rows (n = 10) and see if we can find the outlier.
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head(nestling_weight2, n = 10)# A tibble: 10 × 3
year weight total
<int> <chr> <dbl>
1 1977 mean_day_1 21.8
2 1978 mean_day_1 1.76
3 1979 mean_day_1 1.69
4 1980 mean_day_1 2.18
5 1981 mean_day_1 2.03
6 1982 mean_day_1 2.54
7 1983 mean_day_1 1.90
8 1984 mean_day_1 1.57
9 1985 mean_day_1 1.68
10 1986 mean_day_1 1.59Ah ha! The first row contains a mean_day_1 weight of 21.8. This is likely an error. Let’s get rid of it and then re-plot it.
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nestling_weight2 <- nestling_weight2[-1,]
ggplot(data = nestling_weight2,
aes(x = year, y = total, group = weight)) + #Year goes on the x axis, weight(totals) on the y axis, and we group by each the means of each day
geom_line(aes(linetype = weight, color = weight)) + #aes changes the aesthetics of the lines so that linetype and colors are different from each other
theme_classic()
Much better!
Questions
Do you see any trends within this datasets over time?
What other variables could you look at within the nestling dataset?
Check out this cool video on nesting Tree Swallows!